Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
Studieoversigt
Status
Status
Betingelser
Betingelser
Intervention / Behandling
Intervention / Behandling
Detaljeret beskrivelse
- Recognition and segmentation of anterior talofibular ligament based on DenseNet. Densenet was used to recognize the axial T2-fs image, and the image level was the most typical one. The labelimg program based on Python was used to locate the coordinates of the anterior talofibular ligament and then imported into Python for learning. All the data were divided into a training set (70%, and then 30% of the training set was selected as the verification set). The remaining 30% was used as the test set to evaluate the accuracy of model recognition. After identifying the anterior talofibular ligament, the local clipping and amplification are carried out to remove the redundant information. Finally, input the result to the next step.
- Establishment and comparison of various deep learning models: four deep learning models were established and compared in this study, namely VGG19, AlexNet, CapsNet, and GoogleNet. The models using image fitting alone and those combining with clinical physical examination data were compared for each deep learning model. The diagnostic efficiency between models was expressed by the ROC curve, including AUC, F1 score, etc. the ROC curve was further analyzed by t-test, Delong test, and other statistical methods. In this study, the data were divided into a training set (70%, 30% in the training set as the validation set), and the remaining 30% as the test set to evaluate the classification accuracy.
Undersøgelsestype
Undersøgelsestype
Tilmelding (Forventet)
Tilmelding
Kontakter og lokationer
Studiekontakt
Studiekontakt
- Navn: huishu Yuan, MD
- Telefonnummer: 15810245738
- E-mail: huishuy@bjmu.edu.cn
Undersøgelse Kontakt Backup
- Navn: Ming Ni, MD
- Telefonnummer: 13884794867
- E-mail: sdyingxiang2017@163.com
Studiesteder
-
-
Beijing
-
Beijing, Beijing, Kina, 010
- Rekruttering
- Peking University Third Hospital
-
Kontakt:
- Huishu Yuan, Dr
- E-mail: huishuy@bjmu.edu.cn
-
-
Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Barn
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Køn, der er berettiget til at studere
Prøveudtagningsmetode
Studiebefolkning
Beskrivelse
Inclusion Criteria:
- Without any treatment before imaging examination;
- MR of ankle joint was performed within 3 months before operation and the image quality was good;
- Arthroscopic operation was performed in our hospital and the operation records were complete.
Exclusion Criteria:
- history of ankle surgery, history of cancer or previous fractures.
- Unclear image, serious artifact or incomplete clinical data.
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Antal grupper/kohorter
Kohorter og interventioner
Gruppe / kohorteGruppe / kohorte |
Intervention / BehandlingIntervention / Behandling |
|---|---|
|
Normal control group-Grade 0
Arthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear.
|
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
|
|
Ligament injury -Grade 1
Arthroscopic examination of the ankle joint showed ligament degeneration or injury, but no local or complete tear.
|
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
|
|
Ligament tear-Grade 2
Arthroscopy of the ankle joint revealed partial or complete loss of ligaments.
|
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
|
Hvad måler undersøgelsen?
Primære resultatmål
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
Tidsramme: 2021.1-2022.3.1
|
The model of deep learning was obtained for diagnosis and grading of anterior fibular ligament and compared with the doctors of different grades.
|
2021.1-2022.3.1
|
Samarbejdspartnere og efterforskere
Sponsor
Sponsor
Efterforskere
Efterforskere
- Studiestol: huishu Yuan, MD, Peking University Third Hospital
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Studiestart
Primær færdiggørelse (Forventet)
Primær færdiggørelse
Studieafslutning (Forventet)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- M2020460
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
Studerer et amerikansk FDA-reguleret lægemiddelprodukt
Studerer et amerikansk FDA-reguleret enhedsprodukt
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